


'''simple docstring'''



import unittest

import numpy as np
from datasets import load_dataset

from transformers.testing_utils import require_torch, require_vision
from transformers.utils import is_torch_available, is_vision_available

from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs


if is_torch_available():
		import torch

if is_vision_available():
		from PIL import Image

		from transformers import BeitImageProcessor


class 		_snake_case       (  unittest.TestCase						):




					def __init__(   self				,				_lowerCamelCase				,				_lowerCamelCase=7				,				_lowerCamelCase=3				,				_lowerCamelCase=18				,				_lowerCamelCase=30				,				_lowerCamelCase=400				,				_lowerCamelCase=True				,				_lowerCamelCase=None				,				_lowerCamelCase=True				,				_lowerCamelCase=None				,				_lowerCamelCase=True				,				_lowerCamelCase=[0.5, 0.5, 0.5]				,				_lowerCamelCase=[0.5, 0.5, 0.5]				,				_lowerCamelCase=False				,				):
								UpperCAmelCase__	:       List[str]   =  size if size is not None else {'''height''': 20, '''width''': 20}
								UpperCAmelCase__	:       int   =  crop_size if crop_size is not None else {'''height''': 18, '''width''': 18}
								UpperCAmelCase__	:       List[str]   =  parent
								UpperCAmelCase__	:       Any   =  batch_size
								UpperCAmelCase__	:       Any   =  num_channels
								UpperCAmelCase__	:       Union[str, Any]   =  image_size
								UpperCAmelCase__	:       List[str]   =  min_resolution
								UpperCAmelCase__	:       Dict   =  max_resolution
								UpperCAmelCase__	:       Tuple   =  do_resize
								UpperCAmelCase__	:       List[str]   =  size
								UpperCAmelCase__	:       int   =  do_center_crop
								UpperCAmelCase__	:       Optional[int]   =  crop_size
								UpperCAmelCase__	:       Union[str, Any]   =  do_normalize
								UpperCAmelCase__	:       Optional[int]   =  image_mean
								UpperCAmelCase__	:       List[str]   =  image_std
								UpperCAmelCase__	:       str   =  do_reduce_labels




					def 					snake_case__    (   self):
								return {
								    "do_resize": self.do_resize,
								    "size": self.size,
								    "do_center_crop": self.do_center_crop,
								    "crop_size": self.crop_size,
								    "do_normalize": self.do_normalize,
								    "image_mean": self.image_mean,
								    "image_std": self.image_std,
								    "do_reduce_labels": self.do_reduce_labels,
								}







def      _UpperCamelCase   (		):
			UpperCAmelCase__	:       Optional[Any]   =  load_dataset("""hf-internal-testing/fixtures_ade20k"""			,   split="""test"""       )

			UpperCAmelCase__	:       int   =  Image.open(dataset[0]["""file"""]       )
			UpperCAmelCase__	:       Optional[int]   =  Image.open(dataset[1]["""file"""]       )

			return image, map







def      _UpperCamelCase   (		):
			UpperCAmelCase__	:       int   =  load_dataset("""hf-internal-testing/fixtures_ade20k"""			,   split="""test"""       )

			UpperCAmelCase__	:       int   =  Image.open(ds[0]["""file"""]       )
			UpperCAmelCase__	:       Optional[int]   =  Image.open(ds[1]["""file"""]       )
			UpperCAmelCase__	:       List[str]   =  Image.open(ds[2]["""file"""]       )
			UpperCAmelCase__	:       List[str]   =  Image.open(ds[3]["""file"""]       )

			return [imagea, imagea], [mapa, mapa]


@require_torch
@require_vision
class 		_snake_case       (  _SCREAMING_SNAKE_CASE							, unittest.TestCase						):
					lowerCAmelCase   :Optional[Any]				=       BeitImageProcessor if is_vision_available() else None




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Optional[int]   =  BeitImageProcessingTester(self)




					@property
					def 					snake_case__    (   self):
								return self.image_processor_tester.prepare_image_processor_dict()




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       List[str]   =  self.image_processing_class(**self.image_processor_dict)
								self.assertTrue(hasattr(__UpperCAmelCase				,				"""do_resize"""))
								self.assertTrue(hasattr(__UpperCAmelCase				,				"""size"""))
								self.assertTrue(hasattr(__UpperCAmelCase				,				"""do_center_crop"""))
								self.assertTrue(hasattr(__UpperCAmelCase				,				"""center_crop"""))
								self.assertTrue(hasattr(__UpperCAmelCase				,				"""do_normalize"""))
								self.assertTrue(hasattr(__UpperCAmelCase				,				"""image_mean"""))
								self.assertTrue(hasattr(__UpperCAmelCase				,				"""image_std"""))




					def 					snake_case__    (   self):
								UpperCAmelCase__	:       Optional[int]   =  self.image_processing_class.from_dict(self.image_processor_dict)
								self.assertEqual(image_processor.size				,				{"""height""": 20, """width""": 20})
								self.assertEqual(image_processor.crop_size				,				{"""height""": 18, """width""": 18})
								self.assertEqual(image_processor.do_reduce_labels				,				__UpperCAmelCase)

								UpperCAmelCase__	:       Tuple   =  self.image_processing_class.from_dict(
								    self.image_processor_dict				,				size=42				,				crop_size=84				,				reduce_labels=__UpperCAmelCase)
								self.assertEqual(image_processor.size				,				{"""height""": 42, """width""": 42})
								self.assertEqual(image_processor.crop_size				,				{"""height""": 84, """width""": 84})
								self.assertEqual(image_processor.do_reduce_labels				,				__UpperCAmelCase)




					def 					snake_case__    (   self):
								pass




					def 					snake_case__    (   self):
								# Initialize image_processing
								UpperCAmelCase__	:       Any   =  self.image_processing_class(**self.image_processor_dict)
								# create random PIL images
								UpperCAmelCase__	:       Union[str, Any]   =  prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__UpperCAmelCase)
								for image in image_inputs:
											self.assertIsInstance(__UpperCAmelCase				,				Image.Image)

								# Test not batched input
								UpperCAmelCase__	:       Dict   =  image_processing(image_inputs[0]				,				return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)

								# Test batched
								UpperCAmelCase__	:       str   =  image_processing(__UpperCAmelCase				,				return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)




					def 					snake_case__    (   self):
								# Initialize image_processing
								UpperCAmelCase__	:       str   =  self.image_processing_class(**self.image_processor_dict)
								# create random numpy tensors
								UpperCAmelCase__	:       Any   =  prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__UpperCAmelCase				,				numpify=__UpperCAmelCase)
								for image in image_inputs:
											self.assertIsInstance(__UpperCAmelCase				,				np.ndarray)

								# Test not batched input
								UpperCAmelCase__	:       Tuple   =  image_processing(image_inputs[0]				,				return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)

								# Test batched
								UpperCAmelCase__	:       int   =  image_processing(__UpperCAmelCase				,				return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)




					def 					snake_case__    (   self):
								# Initialize image_processing
								UpperCAmelCase__	:       Tuple   =  self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								UpperCAmelCase__	:       Tuple   =  prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__UpperCAmelCase				,				torchify=__UpperCAmelCase)
								for image in image_inputs:
											self.assertIsInstance(__UpperCAmelCase				,				torch.Tensor)

								# Test not batched input
								UpperCAmelCase__	:       Optional[Any]   =  image_processing(image_inputs[0]				,				return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)

								# Test batched
								UpperCAmelCase__	:       Tuple   =  image_processing(__UpperCAmelCase				,				return_tensors="""pt""").pixel_values
								self.assertEqual(
								    encoded_images.shape				,				(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)




					def 					snake_case__    (   self):
								# Initialize image_processing
								UpperCAmelCase__	:       str   =  self.image_processing_class(**self.image_processor_dict)
								# create random PyTorch tensors
								UpperCAmelCase__	:       Tuple   =  prepare_image_inputs(self.image_processor_tester				,				equal_resolution=__UpperCAmelCase				,				torchify=__UpperCAmelCase)
								UpperCAmelCase__	:       int   =  []
								for image in image_inputs:
											self.assertIsInstance(__UpperCAmelCase				,				torch.Tensor)
											maps.append(torch.zeros(image.shape[-2:]).long())

								# Test not batched input
								UpperCAmelCase__	:       str   =  image_processing(image_inputs[0]				,				maps[0]				,				return_tensors="""pt""")
								self.assertEqual(
								    encoding["""pixel_values"""].shape				,				(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)
								self.assertEqual(
								    encoding["""labels"""].shape				,				(
								        1,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)
								self.assertEqual(encoding["""labels"""].dtype				,				torch.long)
								self.assertTrue(encoding["""labels"""].min().item() >= 0)
								self.assertTrue(encoding["""labels"""].max().item() <= 255)

								# Test batched
								UpperCAmelCase__	:       List[Any]   =  image_processing(__UpperCAmelCase				,				__UpperCAmelCase				,				return_tensors="""pt""")
								self.assertEqual(
								    encoding["""pixel_values"""].shape				,				(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)
								self.assertEqual(
								    encoding["""labels"""].shape				,				(
								        self.image_processor_tester.batch_size,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)
								self.assertEqual(encoding["""labels"""].dtype				,				torch.long)
								self.assertTrue(encoding["""labels"""].min().item() >= 0)
								self.assertTrue(encoding["""labels"""].max().item() <= 255)

								# Test not batched input (PIL images)
								UpperCAmelCase__	:       Optional[Any]   =  prepare_semantic_single_inputs()

								UpperCAmelCase__	:       Tuple   =  image_processing(__UpperCAmelCase				,				__UpperCAmelCase				,				return_tensors="""pt""")
								self.assertEqual(
								    encoding["""pixel_values"""].shape				,				(
								        1,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)
								self.assertEqual(
								    encoding["""labels"""].shape				,				(
								        1,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)
								self.assertEqual(encoding["""labels"""].dtype				,				torch.long)
								self.assertTrue(encoding["""labels"""].min().item() >= 0)
								self.assertTrue(encoding["""labels"""].max().item() <= 255)

								# Test batched input (PIL images)
								UpperCAmelCase__	:       Dict   =  prepare_semantic_batch_inputs()

								UpperCAmelCase__	:       Dict   =  image_processing(__UpperCAmelCase				,				__UpperCAmelCase				,				return_tensors="""pt""")
								self.assertEqual(
								    encoding["""pixel_values"""].shape				,				(
								        2,
								        self.image_processor_tester.num_channels,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)
								self.assertEqual(
								    encoding["""labels"""].shape				,				(
								        2,
								        self.image_processor_tester.crop_size["""height"""],
								        self.image_processor_tester.crop_size["""width"""],
								    )				,				)
								self.assertEqual(encoding["""labels"""].dtype				,				torch.long)
								self.assertTrue(encoding["""labels"""].min().item() >= 0)
								self.assertTrue(encoding["""labels"""].max().item() <= 255)




					def 					snake_case__    (   self):
								# Initialize image_processing
								UpperCAmelCase__	:       int   =  self.image_processing_class(**self.image_processor_dict)

								# ADE20k has 150 classes, and the background is included, so labels should be between 0 and 150
								UpperCAmelCase__	:       str   =  prepare_semantic_single_inputs()
								UpperCAmelCase__	:       Any   =  image_processing(__UpperCAmelCase				,				__UpperCAmelCase				,				return_tensors="""pt""")
								self.assertTrue(encoding["""labels"""].min().item() >= 0)
								self.assertTrue(encoding["""labels"""].max().item() <= 150)

								UpperCAmelCase__	:       str   =  True
								UpperCAmelCase__	:       Union[str, Any]   =  image_processing(__UpperCAmelCase				,				__UpperCAmelCase				,				return_tensors="""pt""")
								self.assertTrue(encoding["""labels"""].min().item() >= 0)
								self.assertTrue(encoding["""labels"""].max().item() <= 255)


import unittest

from transformers import load_tool
from transformers.utils import is_torch_available


if is_torch_available():
		import torch

from transformers.testing_utils import require_torch

from .test_tools_common import ToolTesterMixin







@require_torch
class 					__lowercase  (						unittest.TestCase		,       _SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							def       snake_case			(					self						)		->       Optional[Any]:
														A      :					Optional[Any]      		=					load_tool('''text-to-speech'''						)
														self.tool.setup()




							def       snake_case			(					self						)		->       Union[str, Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					Optional[Any]      		=					self.tool('''hey'''						)
														A      :					List[str]      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






							def       snake_case			(					self						)		->       Optional[Any]:
														# SpeechT5 isn't deterministic
														torch.manual_seed(0						)
														A      :					int      		=					self.tool('''hey'''						)
														A      :					Any      		=					result.to_raw()
														self.assertTrue(
														    torch.allclose(
														        resulting_tensor[:3]    ,						torch.tensor([-0.0_0_0_5_9_6_6_6_6_8_8_3_2_1_1_5_8_2_9, -0.0_0_0_3_6_5_7_6_4_0_1_9_0_7_9_5_0_6_4, -0.0_0_0_1_3_4_3_9_5_0_2_7_9_9_8_8_3_4_8_5]						)    ,						)						)






